We assume that each spiking unit \(i\)  is either in an armed state \(x_i=0\) , capable of spiking, or in a refractory state \(x_i=1\) , representing a unit that just spiked and is incapable of spiking again until it recovers. We assume that a unit influences the spiking of a neighboring unit if and only if it is in the refractory state, a reasonable condition also assumed in [1]}. In addition, to account for a well-known observation that the transmission of spikes across synapses is unreliable [2]}, we model the random chance of spiking events that depend on synaptic strengths \(w_{ij}\)  and unit biases \(b_i\)  as stochastic processes. Specifically, the wait time to the next spiking event of unit \(i\)  is assumed to be exponentially distributed with rate
\(\lambda _i(\theta ) = r_i \exp \left\lbrace \sigma _i z_i/\tau \right\rbrace ,\) 
